Résumé: In the field of management research, decision makers would like to be provided with statistical tools that can help them identify risk factors requiring priority action to achieve desirable outcomes such as reducing work-related stress levels. The aim is to identify the best drivers of improvement, and quantify their respective impacts. However, as predictors are often correlated, regression coefficients cannot be used directly to provide decision makers with ranked predictors. To overcome this limit, the Weifila method has been proposed, which is based on variance decomposition in the linear regression context.
Here, we hierarchize risk factors in terms of their impact on the outcome of interest, using four different metrics. The first is based on the Weifila method, the second on random forests, the third on attributable risk (an epidemiological indicator), and the fourth on path coefficients in a PLS-SEM model.
This study was motivated a large work-related stress level dataset with 10,000 anonymized employees who completed two questionnaires in a face-to-face interview with an occupational physician. The first, on 25 stress-related items, was subsequently used to build a stress scale (the outcome of interest). The second questionnaire involved 58 psychosocial risk factors on a 6-points Likert scale.
The results show similar rankings for the ten first items for the four different metrics. The attributable risk is the easiest tool to use for managers, but requires a causal assumption that needs further analysis.